Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Reverse shoulder arthroplasty for fracture: how does a contemporary lateralized prosthesis compare with a Grammont-style implant?

Journal of shoulder and elbow arthroplasty·2026

Same author

Effectiveness of Endoscopic versus Open Carpal Tunnel Release on Severe Carpal Tunnel Syndrome.

Journal of wrist surgery·2026

Same author

Lower Trapezius Transfer at the Time of Reverse Shoulder Arthroplasty with an Allograft-Prosthetic Composite Can Restore External Rotation.

The Journal of bone and joint surgery. American volume·2026

Same author

Concurrent Nerve and Tendon Transfers in a Tetraplegic Patient: Long-Term Functional Recovery and Late Hardware-Associated Flexor Pollicis Longus Rupture.

Cureus·2026

Same author

Operative management of self-inflicted upper extremity amputations: A retrospective case series.

Hand surgery & rehabilitation·2026

Same author

Tendon Transfer Versus Tendon Graft Reconstruction of Flexor Pollicis Longus Attritional Ruptures Secondary to Volar Plating of Distal Radius Fractures.

Hand (New York, N.Y.)·2026

Same journal

Current Concepts in Perioperative Guidance and Outcomes in Hand Surgery Patients Taking Glucagon-Like Peptide-1 Receptor Agonists.

The Journal of hand surgery·2026

Same journal

Mesenchymal Stem Cell-Derived Exosomes Combined With 3-Dimensional Hyaluronan-Based Scaffold Promote Tendon-to-Bone Tunnel Healing.

The Journal of hand surgery·2026

Same journal

The Diagnosis and Management of Clenched Fist Syndrome.

The Journal of hand surgery·2026

Same journal

Ultrasound-Assisted Thin Posteromedial Thigh Free Flap for Coverage of Hand and Upper-Extremity Wounds.

The Journal of hand surgery·2026

Same journal

Comparison of One- and Two-knot Techniques in Eight-Strand Flexor Tendon Repair Using Looped Sutures: A Biomechanical Study in a Porcine Model.

The Journal of hand surgery·2026

Same journal

Biomechanical Comparison of Metal Versus Suture and Screw Tension Band Technique for Olecranon Fractures.

The Journal of hand surgery·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 9, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Machine Learning for Hand Surgeons: Emerging Clinical Applications.

Jacob Zeitlin¹, Tristan B Weir², Andrew J Miller²

¹Philadelphia Hand to Shoulder Center, Thomas Jefferson University Hospital, Philadelphia, PA; Weill Cornell Medicine, New York, NY.

The Journal of Hand Surgery

|May 5, 2025

Summary

This summary is machine-generated.

Machine learning (ML) is revolutionizing hand surgery by improving diagnostics, predicting outcomes, and optimizing resources. Addressing challenges like data bias and model transparency is key to advancing ML for better patient care and efficient practice management.

Keywords:

Artificial intelligence data machine learning outcome prediction risk stratification

More Related Videos

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Related Experiment Videos

Last Updated: May 9, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

Medicine
Computer Science
Surgical Innovation

Background:

Machine learning (ML) offers significant potential to advance hand surgery.
ML applications span diagnostics, risk stratification, outcome prediction, and practice management.
Current ML use cases include predicting outcomes and optimizing surgical scheduling.

Purpose of the Study:

To explore the transformative potential of ML in hand surgery.
To highlight emerging ML applications and their benefits.
To address the challenges and ethical considerations of implementing ML in clinical practice.

Main Methods:

Review of emerging ML applications in hand surgery.
Emphasis on appraising ML research quality using established guidelines.
Discussion of challenges including data quality, bias, and model interpretability.

Main Results:

ML can predict patient outcomes (e.g., after carpal tunnel release).
ML algorithms can optimize surgical scheduling to reduce wait times.
Key challenges include data bias, lack of generalizability, and ethical concerns.

Conclusions:

Collaboration is needed to create diverse, high-quality datasets for ML in hand surgery.
Transparent and explainable ML algorithms are crucial for clinician trust.
Integrating ML into clinical workflows can enhance evidence-based, individualized patient care.